Search Results (2,537)

The accelerating integration of renewable energy sources into modern power grids has created unprecedented operational challenges, with significant system cost volatility under extreme uncertainty events. To address this challenge, this paper presents a risk-aware stochastic approximate dynamic programming (SADP) algorithm based on machine learning and parallel computing architectures. The algorithm learns optimal coordination strategies for source-grid-load-storage resources while explicitly quantifying and mitigating tail risk events that conventional approaches overlook. First, a risk-averse stochastic optimization model is constructed, which captures the complex interdependencies between renewable generation uncertainty, demand variability, and flexible resource coordination through second-order cone programming formulations. This model integrates the GlueVaR (Glued Value-at-Risk) metric, enabling simultaneous optimization across multiple risk horizons with adjustable conservatism parameters. Second, to solve the established model efficiently, an SADP algorithm based on risk-averse approximate value functions (RAVFs) is proposed, in which the training process of the RAVFs employs machine learning principles to directly encode risk preferences into operational decisions. By integrating GlueVaR into offline training across 5000 probabilistically weighted scenarios, the algorithm discovers emergent coordination patterns between distributed resources, which are rarely identified by human operators. Third, a large-scale parallel computing architecture is implemented for the SADP algorithm. This architecture decomposes the multi-period optimization problem into single-period coordinated sub-problems. During offline training, parallel computing of a series of single-period sub-problems can be performed across all probabilistic scenarios, significantly reducing training time. Extensive validation on both the modified IEEE 33-bus and 69-bus systems with integrated wind turbines, photovoltaic plants, energy storage systems, and demand response capabilities demonstrates remarkable performance improvements. Convergence analysis reveals that the AVFs stabilize within 30 training iterations, achieving sub-160 s solution times in online application even for complex networks with heterogeneous resources. By enabling real-time risk-aware decision-making under severe uncertainty, the proposed method provides grid operators with actionable strategies that balance economic efficiency and operational resilience. Full article

This paper presents an energy-recovering active snubber in which the energy captured by the snubber capacitor is transferred back to the converter’s input using an auxiliary inverting switching stage. The snubber operates independently of the main power stage and can therefore be applied to a wide range of isolated converter topologies without modifying their primary control or structure. The proposed snubber achieves an energy-recovery efficiency of approximately 80%, thereby reducing snubber-related losses by the same proportion. As a representative implementation, the concept was experimentally validated in a 550 W dual-phase interleaved DC–DC flyback converter, where it improves the overall converter efficiency by 1.6 percentage points and reduces total losses by 18% compared with a dissipative snubber solution. The proposed snubber supports a wide input-voltage range and is well suited for multiphase converters, as most of its components can be shared between phases. Full article

The efficient capture of carbon dioxide (CO₂) using polymer, supported ionic liquids (ILs) remains challenging due to limited understanding of atomic-scale interaction mechanisms. Here, a polystyrene (PS) oligomer supported by an amidinium chloride-based IL is proposed as a CO₂-absorbing material. Density functional theory (DFT) calculations were employed to investigate the structural, electronic, and intermolecular interaction energy characteristics of the PS oligomer, amidinium chloride ILs, CO₂, and their binary and ternary complexes. Molecular electrostatic potential maps (MEPs), reduced density gradient (RDG) plots with non-covalent interaction (NCI) snapshots, quantum theory of atoms in molecules critical point (CP) analysis, and electron localization function (ELF) analysis reveal pronounced hydrogen bonding and dispersion interactions between PS and IL that modulate the electronic environment of the IL anion, which is the primary CO₂ binding site. Interaction energy calculations show that the ternary PS–IL–CO₂ complex exhibits a significantly enhanced binding energy compared to the isolated IL–CO₂ complex, providing quantitative evidence for the cooperative role of the PS support. The results indicate enhanced CO₂ binding in the presence of PS supported by ILs, driven by cooperative electrostatic and dispersion interactions. These findings provide molecular-level insights into CO₂ capture mechanisms in polymer–IL hybrid systems. Full article

This paper presents a comprehensive three-dimensional numerical investigation of a novel vertical-axis wind turbine (VAWT) characterised by a unique aerodynamic profile and a passive blade-pitch control mechanism. Unlike conventional fixed-geometry designs, the proposed turbine utilizes rectangular blades mounted on horizontal axes via articulated bearings, allowing them to rotate freely up to 90 degrees, constrained by a vertical pin-and-belt system. This configuration ensures that blades on the power-stroke side hit the vertical stopper to capture maximum wind energy, while blades on the return-stroke side open up to 90 degrees to significantly reduce aerodynamic drag. This dynamic adjustment enables the turbine to operate efficiently in low-wind conditions (3–5 m/s) while maintaining enhanced torque stability. To ensure numerical reliability, a rigorous grid independence study was performed, and the computational domain was configured to eliminate wall interference effects. The aerodynamic performance was analyzed using COMSOL Multiphysics v6.2 by solving the Reynolds-averaged Navier–Stokes (RANS) equations. Four turbulence models—SST, k–ε, k–ω, and RNG—were evaluated, with the SST model demonstrating the highest fidelity in capturing flow separation and wake structures under adverse pressure gradients. This study establishes the turbine’s performance benchmarks, including the power coefficient (C_p) versus tip speed ratio (TSR) curves. The numerical results were validated against laboratory experimental data, with excellent agreement (relative error < 5%). The findings identify the optimal geometric parameters and tangential velocity distributions that distinguish this configuration (Patent FAP 20240465) from traditional VAWTs. Finally, the successful implementation of a 2 kW prototype confirms the model’s accuracy and highlights the turbine’s potential as a stable and efficient solution for sustainable urban energy harvesting. Full article

This paper compares two combined methods for modeling correlations among multiple random variables in the state-space sampling approach that is adopted in non-sequential Monte Carlo Simulation. The first method combines the Rank Score (Iman and Conover method) with Latin Hypercube sampling, while the second method combines Kernel Density Estimation with Bayesian Networks to represent dependency between variables. The methods are compared in terms of goodness-of-fit and computational efficiency, using statistical metrics, spatial correlation matrices, and scatter plots. The multiple time series were aggregated into single ones (Total and Net Generation) in order to validate the accuracy of each model in representing multi-dimensional correlated variables. The methods were evaluated for 3 large-dimensional cases based on the actual Brazilian energy system, with 10, 19 and 28 time series of 5-, 3- and 2-year horizons, respectively. The results show that the statistical dependencies of the historical data were accurately captured by both models, with comparable goodness-of-fit, but a higher computational efficiency of the first. Full article

A significant amount of mechanical and thermal energy is lost when typing on a laptop keyboard. To address this, hybrid energy harvesters must increase the generated power density and mitigate energy fluctuation issues. This paper explores the potential enhancement of energy harvesting by combining thermoelectric and piezoelectric effects within a multilayered structure integrated into a laptop keyboard button. Through numerical simulation, the study assesses how these two behaviors can synergistically increase the power density generated by the hybrid device. The focus is on optimizing energy efficiency by harnessing the heat losses from integrated circuits and the mechanical stresses due to the act of typing. The point is to refine the design of such a system to maximize the conversion of ambient energy into electricity. The findings indicate that the hybrid structure combining both piezoelectric and thermoelectric effects, effectively captures energy from a laptop keyboard, producing a substantial amount of electricity. This investigation shows that the generator can produce up to 2.07 mW of power using PU-40%PZT as piezoelectric material and an additional 71.93 μW through the PEDOT: PSS as thermoelectric material from a single keystroke when pressed and heated. This study underscores the potential for improving energy-harvesting efficiency in laptop keyboards, contributing to more sustainable and energy-efficient electronic devices. Full article

This study investigates the asymmetric and heterogeneous effects of renewable energy, non-renewable energy, capital stock, labour, and trade openness on CO₂ emissions in BRICS countries over the period 1991–2022. The study applies a panel nonlinear autoregressive distributed lag (PNARDL) model to capture short- and long-run asymmetries, complemented by a panel quantile nonlinear ARDL (QNARDL) to assess distributional heterogeneity. Robustness is ensured using Fully Modified Ordinary Least Squares (FMOLS) and Robust Least Squares (RLS) estimators. The study is grounded in the Environmental Kuznets Curve (EKC) and Just Energy Transition Theory. The results reveal a stable long-run cointegrating relationship among the variables, with a significant error correction mechanism confirming convergence toward equilibrium. Renewable energy consumption consistently reduces CO₂ emissions in both the short and long run, while non-renewable energy significantly increases emissions, exhibiting strong asymmetric effects. Capital stock shows mixed dynamics, increasing emissions in the short run but reducing them in the long run when directed toward productive and efficient investments. Labour is found to reduce emissions in the long run, highlighting the role of human capital in supporting cleaner production. Trade openness generally increases emissions, reflecting energy-intensive trade structures. Quantile results confirm heterogeneity, with stronger renewable energy effects at higher emission levels and greater environmental gains from reducing fossil fuel dependence than from increasing it. The FMOLS and RLS estimations confirm robustness, reinforcing the negative relationship between renewable energy and emissions and the positive impact of non-renewable energy. The study recommends accelerated renewable energy deployment, fossil fuel phase-down strategies, and targeted green capital investment. It further emphasizes grid modernization and energy storage systems to enhance renewable integration, alongside labour reskilling and green trade policies. These coordinated strategies are essential for achieving sustainable decarbonization in BRICS economies. Full article

The article addresses the assessment of energy security instability associated with the energy transition process in the EU-27 countries under decarbonisation conditions. It introduces the original Dynamic Energy Security Risk Index (DESRI), interpreted as a synthetic measure of transition-related instability in the field of energy security. The index is structured around five analytical pillars: external dependence and security of supply, the climate and emissions dimension, efficiency and demand-related factors, structural and transformational characteristics of the energy mix, and the economic and social dimension. The empirical analysis covers the EU-27 countries over the period 2013–2023 and is based on 17 indicators obtained from European statistical sources. The dynamic nature of the model is captured through logarithmic rates of change, volatility analysis using a rolling time window, and the multilevel aggregation of instability components at the indicator and pillar levels. The results demonstrate that transition-related instability in the European Union varies considerably across both countries and time. The lowest DESRI values were recorded, among others, in Italy in 2018, Portugal in 2021, Belgium in 2017, and Austria in 2016, indicating relatively stable transition trajectories in those years. By contrast, the highest levels of instability occurred in Estonia in 2017–2018 and Luxembourg in 2022–2023, reflecting rapid and irregular changes in selected dimensions of the energy transition. The analysis also revealed periods of accumulated transition-related instability, particularly in 2017–2019 and 2021–2022, when the irregularity of transition pathways increased simultaneously in numerous countries. These findings show that energy transition-related instability depends not only on the level of transition advancement but also on the pace, volatility, and irregularity of structural changes. Countries with a relatively favourable static energy security or decarbonisation profile may therefore exhibit elevated dynamic instability when the transition proceeds rapidly or unevenly or requires intensive infrastructural, regulatory, and social adjustments. The article’s main contribution is the development of a replicable dynamic assessment framework that complements conventional static approaches to energy security analysis by identifying instability embedded in transition trajectories. DESRI provides an additional comparative perspective for monitoring the energy transition in the European Union. It may also support the identification of countries and policy areas requiring particular attention regarding security of supply, decarbonisation, demand-side efficiency, structural change, and the social acceptability of transition costs. Full article

DC microgrids are important platforms for renewable energy integration, energy storage interaction, and bidirectional power exchange. In these systems, multi-phase interleaved parallel DC-DC converters are widely used as key energy-router interfaces, but open-circuit faults in power devices may lead to current imbalance, waveform distortion, ripple redistribution, and system instability. To improve fault warning and diagnosis under variable operating conditions, this paper proposes a time–frequency dual-branch efficient fully convolutional network (EPFCN). The proposed model takes synchronized multi-channel voltage/current signals and their FFT-domain representations as complementary inputs. The time-domain branch extracts transient waveform features, while the FFT-domain branch captures spectral variation and harmonic-related information. An efficient channel attention (ECA) module is introduced to enhance fault-sensitive channel responses while maintaining a lightweight structure. An RT-LAB hardware-in-the-loop platform is established to construct a multi-condition diagnostic dataset covering one normal state and nine fault states. Experimental results show that the proposed EPFCN achieves high diagnostic accuracy, strong noise robustness, clear feature separability, and feasible edge-side inference performance. The proposed method provides an effective data-driven solution for online fault warning and diagnosis of multi-phase interleaved converters in DC microgrids. Full article

The transition toward next-generation energy systems requires advanced computational tools capable of supporting accurate, adaptive, and data-driven energy performance assessment. Within this context, Energy Performance Indicators (EnPIs) established under the ISO 50001 framework remain essential for monitoring energy efficiency and continuous improvement; however, conventional indicators are often based on static or simplified relationships that do not adequately capture the dynamic, nonlinear, and multivariable behavior of modern buildings and energy management systems. This systematic review analyzes the integration of ISO 50001-based EnPIs with intelligent algorithms and artificial intelligence techniques for enhanced energy management. The review follows a PRISMA-inspired methodology, using Scopus as the primary database and Web of Science and Google Scholar as complementary sources. From 5442 initial records, 2691 studies were screened and 283 articles were selected for detailed analysis, supported by a bibliometric keyword co-occurrence analysis using VOSviewer 1.6.20. The results show a clear evolution from traditional energy indicators and normalized baselines toward computational modeling approaches based on regression analysis, machine learning, deep learning, forecasting, anomaly detection, and optimization algorithms. These methods improve the predictive capability, adaptability, and operational relevance of EnPIs by incorporating climatic, occupancy, temporal, and operational variables. The reviewed evidence indicates that intelligent algorithms can strengthen ISO 50001 energy management systems by enabling dynamic baselines, early detection of abnormal consumption patterns, predictive decision-making, and continuous operational optimization. Nevertheless, challenges remain regarding data quality, model interpretability, methodological standardization, and practical integration into certified energy management frameworks. Overall, this review highlights that the future of energy performance assessment does not rely on replacing conventional EnPIs, but on transforming them into intelligent, computationally supported indicators for sustainable, resilient, and next-generation energy management systems. Full article

AVs are also bound to capitalize on 5G networks, which creates crucial challenges in the adaptable management of resources because they need very low latency, a high-speed connection, and energy-efficient functionality. Older approaches to optimizing resource allocation in the high-frequency changing vehicle environment fail to deliver as mobility trends and network status constantly adapt and change. To overcome these problems, we suggest a new Deep Reinforcement Learning (DRL)-based algorithm, which is aimed at optimizing the allocation of resources to AVs. This model combines a Spatiotemporal Graph Convolution Network (ST-GCN), Gated Recurrent Units (GRU), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to create a unified model. The ST-GCN is successful at both capturing the dynamic space relationship between vehicles and between vehicles and roadside infrastructure, and also gives a complete picture of network topology. GRU uses traffic and communication information to forecast future mobility patterns and bandwidth demand of each agent and therefore allocate resources proactively. The MADDPG algorithm is used to enable decentralized but coordinated decision-making among AVs, which enables the realization of dynamic policies of bandwidth allocation in real-time. Simulations using such aspects as a realistic Rayleigh fading channel model, a node density of 100 vehicles/km², and 100 MHz of bandwidth prove the effectiveness of the framework extensively. We find that the end-to-end latency increase is reduced by up to 30%, and the system throughput is increased by up to 28, and the energy efficiency is increased by an average of 40 percent in comparison with the baseline techniques. Such results confirm our framework to be a plausible solution to building effective and sustainable communication systems to enable AVs to cooperate in the information exchange of important data. Full article

Municipal wastewater treatment plants (WWTPs) are essential for protecting public health, however, their contribution to greenhouse gas (GHG) emissions has often been overlooked. Achieving carbon-neutral operation requires more than incremental improvements in energy efficiency; it calls for a rethinking of process design, energy flows, and resource recovery strategies. This review examines recent developments across several key pathways, including carbon capture through A-B configurations, energy recovery via anaerobic digestion, and low-carbon nitrogen removal based on autotrophic processes such as partial nitritation–anammox. Emerging technologies, such as microalgal and bioelectrochemical systems, are also reviewed, although their large-scale applicability remains uncertain. Particular attention is given to the trade-offs introduced by advanced treatment for micropollutant removal, which can significantly increase energy demand if not carefully integrated. Beyond individual technologies, the paper highlights the importance of system-level optimization, life-cycle assessment, and data-driven control strategies. A staged roadmap is proposed to distinguish near-term improvements from longer-term transitions. Rather than presenting a single solution, the review emphasizes that feasible pathways depend strongly on local conditions, including influent characteristics, climate, and energy mix. Full article

Bluetooth Low Energy (BLE) is widely used in Internet of Things (IoT) devices due to its low power consumption and efficient wireless communication. However, BLE-based systems remain vulnerable to signal-level attacks, such as spoofing and signal forgery, which allow adversaries to impersonate legitimate devices and compromise system security. Existing security approaches mainly rely on cryptographic mechanisms or protocol-level features, while conventional signal fingerprinting methods often fail to capture subtle device-specific variations across the frequency spectrum. We propose a deep-learning-based BLE signal fingerprinting framework that uses emphasized spectral data to enhance device authentication. The proposed framework selectively highlights frequency regions exhibiting pronounced hardware-dependent variations using a hybrid filter bank design and extracts spectral features for anomaly-based device identification. Experimental evaluations conducted on BLE signals collected from multiple devices demonstrate that the proposed approach outperforms conventional methods, achieving superior authentication performance. By leveraging emphasized frequency-domain characteristics, we provide an effective authentication method for BLE-based IoT environments. Full article

Solar power generation is highly sensitive to short-term weather variations, particularly under rapid cloud movement, leading to significant power fluctuations and challenges in stable energy dispatch. To address this issue, this study proposes an AI-based solar power prediction and power switching control system integrating a hybrid deep learning model with an embedded microcontroller. The model combines radar echo imagery and meteorological time-series data, where a convolutional neural network (CNN) extracts spatial cloud features and a gated recurrent unit (GRU) captures temporal dynamics for short-term irradiance forecasting. Based on the prediction results, the microcontroller performs real-time power source switching between solar and grid supply. Experimental results using real-world data from the Taiwan Central Weather Bureau demonstrate that the proposed system achieves reliable prediction performance and enables effective proactive energy management. These results suggest that the integration of AI-based forecasting and embedded control has potential for renewable energy utilization and power dispatch applications. By supporting intelligent energy management and more efficient use of photovoltaic energy resources, the proposed framework may contribute to sustainable energy utilization in photovoltaic systems. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. Full article